ReviewBritish journal of cancer2008
Approaches to working in high-dimensional data spaces: gene expression microarrays.
Review in British journal of cancer, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed.
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
- Optimal features selection in the high dimensional data based on robust technique: Application to different health database.Heliyon · 2024Review
- Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors.NPJ systems biology and applications · 2024Article
- Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.BMC bioinformatics · 2024Article
- Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study.Frontiers in big data · 2024Article
- A novel gene selection method for gene expression data for the task of cancer type classification.Biology direct · 2021Article
- A systems biology approach to discovering pathway signaling dysregulation in metastasis.Cancer metastasis reviews · 2020Review
- Multi-view based integrative analysis of gene expression data for identifying biomarkers.Scientific reports · 2019Article
- Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool.Frontiers in genetics · 2019Article
- Statistical Approaches to Address Multi-Pollutant Mixtures and Multiple Exposures: the State of the Science.Current environmental health reports · 2017Review
- Article
- Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures.International journal of genomics · 2017Article
- Study design in high-dimensional classification analysis.Biostatistics (Oxford, England) · 2016Article
- Parallel classification and feature selection in microarray data using SPRINT.Concurrency and computation : practice & experience · 2014Article
- Preclinical magnetic resonance imaging and systems biology in cancer research: current applications and challenges.The American journal of pathology · 2013Review
- Reliably assessing prediction reliability for high dimensional QSAR data.Molecular diversity · 2013Article
- Deconvolving heterogeneity in the CD8+ T-cell response to HIV.Current opinion in HIV and AIDS · 2012Review
- Antiestrogen Resistance and the Application of Systems Biology.Drug discovery today. Disease mechanisms · 2012Article
- A cross-species analysis of a mouse model of breast cancer-specific osteolysis and human bone metastases using gene expression profiling.BMC cancer · 2011Article
- Endoplasmic reticulum stress, the unfolded protein response, and gene network modeling in antiestrogen resistant breast cancer.Hormone molecular biology and clinical investigation · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
This review provides a focused summary of the implications of high-dimensional data spaces produced by gene expression microarrays for building better models of cancer diagnosis, prognosis, and therapeutics. We identify the unique challenges posed by high dimensionality to highlight methodological problems and discuss recent methods in predictive classification, unsupervised subclass discovery, and marker identification.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.